How to select (slice) in Pandas multiple rows and multiple non continuous columns?

Question

I have a dataFrame and I want a range of specific rows and at the same time a range that is composed of a continuous range of columns plus an additional one. Included is a piece of code that produces such a dataFrame

import pandas as pd 
import numpy as np
np.random.seed(5)
dF = pd.DataFrame(np.random.randint(100, size=(100, 6)), 
              columns=list('ABCDEF'), 
              index=['R{}'.format(i) for i in range(100)])
dF.head()

This works fine:

dF.loc[:, 'C':'E']

But I need something like this, that produces an error:

dF.loc['R95':, ['A':'C', 'F']]

The expected result has to include from row 'R95' onwards and columns 'A', 'C' and 'F'

dF.loc['R95':, ['C','D', 'F']] considering "The expected result has to include from row 'R95' onwards and columns 'C', 'D' and 'F'" — anky
– anky, Commented May 31, 2019 at 17:16
@anky_91 I would like to use the ":" within the solution for the selection. — Paradigm
– Paradigm, Commented May 31, 2019 at 17:21
I don't think you can combine slice and commas in the indexing. It's not supported. Read here — Valentino
– Valentino, Commented May 31, 2019 at 17:27

Ben Pap · Accepted Answer · 2019-05-31 18:13:56Z

3

If you can just use the index you can do:

dF.iloc[95:, np.r_[0:3, -1]]

    A   B   C   F
R95 19  7   76  0
R96 57  31  86  64
R97 51  12  59  33
R98 24  7   68  69
R99 81  20  86  70

which is a lot cleaner.

answered May 31, 2019 at 18:13

Ben Pap

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Valentino · Accepted Answer · 2019-05-31 17:37:33Z

pandas indexing allows the following ways to indexing a dataframe (quoting from the docs):

A single label, e.g. 5 or 'a' (Note that 5 is interpreted as a label of the index. This use is not an integer position along the index.).

A list or array of labels ['a', 'b', 'c'].

A slice object with labels 'a':'f' (Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels.).

A boolean array

A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above).

So you need to use something a bit more complex.

For example, you could use pandas.concat to select the columns separately and then join together the dataframes:

pd.concat([dF.loc['R95':, 'A':'C'], dF.loc['R95':,'F']], axis=1)

This gives:

      A   B   C   F
R95  19   7  76   0
R96  57  31  86  64
R97  51  12  59  33
R98  24   7  68  69
R99  81  20  86  70

iamchoosinganame · Accepted Answer · 2019-05-31 17:28:05Z

0

This is an ugly solution but it still has a colon in it

df.loc['R95':, df.loc[:,'A':'C'].columns.tolist()+['F']]

answered May 31, 2019 at 17:28

iamchoosinganame

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